Instructions to use Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M

Use Docker

docker model run hf.co/Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M

Ollama
How to use Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF with Ollama:
```
ollama run hf.co/Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF with Docker Model Runner:
```
docker model run hf.co/Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M
```

Lemonade

How to use Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF / README.md

Andycurrent

Update README.md

9d4a73b verified 9 months ago

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	---
	license: apache-2.0
	language:
	- en
	- zh
	base_model:
	- Qwen/Qwen2.5-VL-7B-Instruct
	tags:
	- Image-to-text
	- text-generation
	- conversational
	- uncensored
	---

	### Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF(Vision Language)

	This repository hosts Qwen2.5-VL-Abliterated-Caption-GGUF, a quantized Vision-Language (Uncensored) model optimized for image understanding and caption generation with relaxed alignment constraints. The model is designed for local inference, experimentation, and research-oriented multimodal workflows.

	It targets users who want direct, descriptive visual reasoning without heavy content moderation layers, packaged in a GGUF format for efficient CPU and edge-device deployment.

	### Model Summary

	- Model Identifier: Qwen2.5-VL-Abliterated-Caption-GGUF
	- Base Model: Qwen2.5-VL (Vision-Language)
	- Architecture: Transformer-based multimodal model (text + vision)
	- Original model: prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF
	- Primary Function: Image captioning and visual-text understanding

	###Purpose & Design Goals

	This variant prioritizes expressive visual descriptions and caption accuracy while minimizing restrictive alignment behaviors. The “abliterated” aspect indicates reduced policy-driven refusals, making the model more suitable for:
	- Dataset generation
	- Visual analysis research
	- Creative or descriptive captioning tasks
	- Offline or private multimodal pipelines

	### Multimodal Interaction Format

	The model follows a standard multimodal prompt structure compatible with Qwen-VL style templates. A typical interaction may include system context, a user query, and an image reference:
	```
	<\|system\|>
	You are a visual captioning assistant.
	<\|user\|>
	Describe the image in detail.
	<\|vision_input\|>
	<image>
	<\|assistant\|>
	```

	### Core Capabilities

	- Detailed and literal image captioning
	- Multimodal reasoning over visual scenes
	- Object, action, and context recognition
	- Long-form descriptive outputs
	- Reduced refusal behavior compared to safety-aligned VL models
	- Optimized for local inference via GGUF

	### Recommended Use Cases

	- Image caption generation – datasets, tagging, annotation
	- Visual analysis – scene breakdowns, object relationships
	- Creative workflows – storytelling from images
	- Research & evaluation – alignment and multimodal behavior testing
	- Offline deployments – no cloud or API dependency


	### Credits & Acknowledgements

	- Qwen team for the base Qwen2.5-VL architecture
	- GGUF tooling and local inference ecosystem contributors
	- Open-source multimodal research community